Definition
The term ‘guided’ in AI typically refers to techniques where the model’s behavior is steered by additional information beyond the primary input. Common examples include guided diffusion, where a classifier or text prompt directs image generation, or guided policy search in reinforcement learning, where high-level plans guide low-level control actions. This approach helps mitigate issues like mode collapse or aimless exploration by providing a structured path toward the desired outcome, improving both the quality and controllability of the AI’s output.
Summary
Describes AI processes or generation methods that are constrained or directed by specific external signals, constraints, or intermediate objectives.
Key Concepts
- Conditional Generation
- Constraint Satisfaction
- Classifier Guidance
- Hierarchical Control
Use Cases
- Text-to-image synthesis with specific styles
- Reinforcement learning with sparse rewards
- Constrained optimization problems